Hadoop入门案列，初学者Coder

1、WordCount

　　Job类：

package com.simope.mr.wcFor;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/**

 * @deprecated 统计文本单词个数

 * @author JimLy

 * @see 20150127

 * */

public class WcForJob {

    public static void main(String[] args) {

        Configuration conf = new Configuration();

        try {

            Job job = new Job(conf);

            job.setJobName("myWC");

            job.setJarByClass(WcForJob.class);

            job.setMapperClass(WcForMapper.class);

            job.setReducerClass(WcForReducer.class);

            job.setMapOutputKeyClass(Text.class);

            job.setMapOutputValueClass(IntWritable.class);

            FileInputFormat.addInputPath(job, new Path("/usr/input/myWc"));

            FileOutputFormat.setOutputPath(job, new Path("/usr/output/myWc"));

            System.exit(job.waitForCompletion(true) ? 0 : 1);

        } catch (Exception e) {

            System.out.println("错误信息:" + e);

        }

    }

}

　　Mapper类：

package com.simope.mr.wcFor;

import java.io.IOException;

import java.util.StringTokenizer;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

public class WcForMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

    @Override

    protected void map(LongWritable key, Text value,

                        Context context)

            throws IOException, InterruptedException {

        String line = value.toString();

        StringTokenizer st = new StringTokenizer(line);

        while (st.hasMoreElements()) {

            context.write(new Text(st.nextToken()), new IntWritable(1));

        }

    }

}

　　Reducer类：

package com.simope.mr.wcFor;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

public class WcForReducer extends Reducer<Text, IntWritable, Text, IntWritable>{

    @Override

    protected void reduce(Text key, Iterable<IntWritable> value,

                            Context context)

            throws IOException, InterruptedException {

        int sum = 0;

        for (IntWritable i : value) {

            sum += i.get();

        }

        context.write(key, new IntWritable(sum));

    }

}

　　文本输入：

　　统计输出：

2、单列排序

　　Job类:

package com.simope.mr.sort;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/**

 * @deprecated 对数字进行排序

 * @author JimLy

 * @see 20160127

 * */

public class SortJob {

    public static void main(String[] args) {

        Configuration conf = new Configuration();

        try {

            Job job = new Job(conf);

            job.setJobName("sortJob");

            job.setJarByClass(SortJob.class);

            job.setMapperClass(SortMapper.class);

            job.setReducerClass(SortReducer.class);

            job.setMapOutputKeyClass(IntWritable.class);

            job.setMapOutputValueClass(IntWritable.class);

            FileInputFormat.addInputPath(job, new Path("/usr/input/sort"));

            FileOutputFormat.setOutputPath(job, new Path("/usr/output/sort"));

            System.exit(job.waitForCompletion(true) ? 0 : 1);

        } catch (Exception e) {

            System.out.println("错误信息:" + e);

        }

    }

}

　　Mapper类：

package com.simope.mr.sort;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

public class SortMapper extends Mapper<LongWritable, Text, IntWritable, IntWritable>{

    @Override

    protected void map(LongWritable key, Text value,

                        Context context)

            throws IOException, InterruptedException {

        String line = value.toString();

        context.write(new IntWritable(Integer.parseInt(line)), new IntWritable(1));

    }

}

　　Reducer类：

package com.simope.mr.sort;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.mapreduce.Reducer;

public class SortReducer extends Reducer<IntWritable, IntWritable, IntWritable, IntWritable> {

    private static IntWritable lineNum = new IntWritable(1); 

    @SuppressWarnings("unused")

    @Override

    protected void reduce(IntWritable key, Iterable<IntWritable> value,

                            Context context)

            throws IOException, InterruptedException {

        //考虑到有相同的值

        for (IntWritable val : value) {

            context.write(lineNum, key);

            lineNum = new IntWritable(lineNum.get() + 1);

        }

    }

}

　　输入文本：

　file1:　file2:file3:

　　统计输出：

3、计算学科平均成绩

　　Job类：

package com.simope.mr.average;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/**

 * @deprecated 计算学生的平均成绩

 * @author JimLy

 * @see 20160127

 * */

public class AveJob {

    public static void main(String[] args) {

        Configuration conf = new Configuration();

        try {

            Job job = new Job(conf);

            job.setJobName("AveJob");

            job.setJarByClass(AveJob.class);

            job.setMapperClass(AveMapper.class);

            job.setReducerClass(AveReducer.class);

            job.setMapOutputKeyClass(Text.class);

            job.setMapOutputValueClass(IntWritable.class);

            FileInputFormat.addInputPath(job, new Path("/usr/input/average"));

            FileOutputFormat.setOutputPath(job, new Path("/usr/output/average"));

            System.exit(job.waitForCompletion(true) ? 0 : 1);

        } catch (Exception e) {

            System.out.println("错误信息:" + e);

        }

    }

}

　　Mapper类：

package com.simope.mr.average;

import java.io.IOException;

import java.io.UnsupportedEncodingException;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

public class AveMapper extends Mapper<LongWritable, Text, Text, IntWritable>{

    String line;

    @Override

    protected void map(LongWritable key, Text value,

                        Context context)

            throws IOException, InterruptedException {

        line = changeTextToUTF8(value, "GBK").toString();

        String[] stuArr = line.split("\t");

        context.write(new Text(stuArr[0]), new IntWritable(Integer.parseInt(stuArr[1])));

    }

    public static Text changeTextToUTF8(Text text, String encoding) {

        String value = null;

        try {

            value = new String(text.getBytes(), 0, text.getLength(), encoding);

        } catch (UnsupportedEncodingException e) {

            e.printStackTrace();

        }

        return new Text(value);

    }

}

　　Reducer类：

package com.simope.mr.average;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

public class AveReducer extends Reducer<Text, IntWritable, Text, IntWritable>{

    int count, sum;

    @Override

    protected void reduce(Text key, Iterable<IntWritable> value,

                            Context context)

            throws IOException, InterruptedException {

        sum = 0;

        count = 0;

        for (IntWritable i : value) {

            count++;

            sum += i.get();

        }

        context.write(key, new IntWritable(sum/count));

    }

}

　　文本输入：

china:english:math:

　　统计输出：

　　附：乱码问题由于hadoop中强制以UTF-8编码格式，而我用的是GBK，未进行转码。

4、族谱：

　　Job类：

package com.simope.mr.grand;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/**

 * @description 家族族谱

 * @author JimLy

 * @see 20160128

 * */

public class GrandJob {

    public static void main(String[] args) {

        Configuration conf = new Configuration();

        try {

            Job job = new Job(conf);

            job.setJobName("GrandJob");

            job.setJarByClass(GrandJob.class);

            job.setMapperClass(GrandMapper.class);

            job.setReducerClass(GrandReducer.class);

            job.setMapOutputKeyClass(Text.class);

            job.setMapOutputValueClass(Text.class);

            FileInputFormat.addInputPath(job, new Path("/usr/input/grand"));

            FileOutputFormat.setOutputPath(job, new Path("/usr/output/grand"));

            System.exit(job.waitForCompletion(true) ? 0 : 1);

        } catch (Exception e) {

            System.out.println("错误信息:" + e);

        }

    }

}

　　Mapper类：

package com.simope.mr.grand;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

public class GrandMapper extends Mapper<LongWritable, Text, Text, Text>{

    @Override

    protected void map(LongWritable key, Text value,

                        Context context)

            throws IOException, InterruptedException {

        String line = value.toString();

        String[] faArr = line.split("\t");

        if (faArr.length == 2) {

            if (!faArr[0].equals("parent")) {

                context.write(new Text(faArr[0]), new Text(faArr[0] + "_" + faArr[1]));

                context.write(new Text(faArr[1]), new Text(faArr[0] + "_" + faArr[1]));

            }

        }

    }

}

　　Reducer类：

package com.simope.mr.grand;

import java.io.IOException;

import java.util.ArrayList;

import java.util.List;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

public class GrandReducer extends Reducer<Text, Text, Text, Text>{

    private static int time = 0; 

    @Override

    protected void reduce(Text key, Iterable<Text> value,

                            Context context) throws IOException,

            InterruptedException {

        List<String> paList = new ArrayList<String>();

        List<String> chList = new ArrayList<String>();

        String info;

        String[] arr;

        for (Text i : value) {

            info = i.toString();

            arr = info.split("_");

            if (arr.length == 2) {

                paList.add(arr[0]);

                chList.add(arr[1]);

            }

        }

        if (time == 0) {

            context.write(new Text("grandParent"), new Text("grandChild"));

            time++;

        }

        for (int i = 0; i < paList.size(); i++) {

            for (int j = 0; j < chList.size(); j++) {

                if (paList.get(i).equals(chList.get(j))) {

                    context.write(new Text(paList.get(j)), new Text(chList.get(i)));

                    time++;

                }

            }

        }

    }

}

　　输入文本：

　　　　file1：file2：

　　统计输出：

　　、

5、二次排序：

　　Job类：

package com.simope.mr.secOrder;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/**

 * @deprecated 二次排序

 * @author JimLy

 * @see 20160129

 * */

public class SecOrderJob {

    public static void main(String[] args) {

        Configuration conf = new Configuration();

        try {

            Job job = new Job(conf);

            job.setJobName("SecOrderJob");

            job.setJarByClass(SecOrderJob.class);

            job.setMapperClass(SecOrderMapper.class);

            job.setReducerClass(SecOrderReducer.class);

            job.setMapOutputKeyClass(IntWritable.class);

            job.setMapOutputValueClass(IntWritable.class);

            FileInputFormat.addInputPath(job, new Path("/usr/input/secOrder"));

            FileOutputFormat.setOutputPath(job, new Path("/usr/output/secOrder"));

            System.exit(job.waitForCompletion(true) ? 0 : 1);

        } catch (Exception e) {

            System.out.println("错误信息:" + e);

        }

    }

}

　　Mapper类：

package com.simope.mr.secOrder;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

public class SecOrderMapper extends Mapper<LongWritable, Text, IntWritable, IntWritable>{

    @Override

    protected void map(LongWritable key, Text value,

                        Context context)

            throws IOException, InterruptedException {

        String line = value.toString();

        String[] numArr = line.split("\t");

        if (numArr.length == 2) {

            context.write(new IntWritable(Integer.parseInt(numArr[0])), new IntWritable(Integer.parseInt(numArr[1])));

        }

    }

}

　　Reducer类：

package com.simope.mr.secOrder;

import java.io.IOException;

import java.util.ArrayList;

import java.util.List;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

public class SecOrderReducer extends Reducer<IntWritable, IntWritable, IntWritable, Text>{

    @Override

    protected void reduce(IntWritable key, Iterable<IntWritable> value,

                Context context)

            throws IOException, InterruptedException {

        String str = "";

        for (IntWritable i : value) {

            str = str + "#" + i.get();

        }

        str = str.substring(1, str.length());

        String[] numArr = str.split("#");

        String temp;

        for (int i = 0; i < numArr.length; i++) {

            for (int j = 0; j < numArr.length; j++) {

                if (Integer.parseInt(numArr[j]) > Integer.parseInt(numArr[i])) {

                    temp = numArr[i];

                    numArr[i] = numArr[j];

                    numArr[j] = temp;

                }

            }

        }

        for (int i = 0; i < numArr.length; i++) {

            context.write(key, new Text(numArr[i]));

        }

    }

}

　　输入文本：

　　统计输出：

6、计算1949-1955年中每年温度最高前10天

　　RunJob类:

package com.simope.mr;

import java.io.IOException;

import java.text.SimpleDateFormat;

import java.util.Calendar;

import java.util.Date;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.Mapper;

import org.apache.hadoop.mapreduce.Reducer;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class RunJob {

    public static SimpleDateFormat SDF = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");

    static class HotMapper extends Mapper<LongWritable, Text, KeyPari, Text> {

        @Override

        protected void map(LongWritable key, Text value,

                Context context)

                throws IOException, InterruptedException {

            String line = value.toString();

            String[] ss = line.split("\t");

            if (ss.length == 2) {

                try {

                    Date date = SDF.parse(ss[0]);

                    Calendar c = Calendar.getInstance();

                    c.setTime(date);

                    int year = c.get(1);

                    String hot = ss[1].substring(0, ss[1].indexOf("C"));

                    KeyPari kp = new KeyPari();

                    kp.setYear(year);

                    kp.setHot(Integer.parseInt(hot));

                    context.write(kp, value);

                } catch (Exception e) {

                    e.printStackTrace();

                }

            }

        }

    }

    static class HotReduce extends Reducer<KeyPari, Text, KeyPari, Text> {

        @Override

        protected void reduce(KeyPari kp, Iterable<Text> value,

            Context context)

                throws IOException, InterruptedException {

            for (Text v :value) {

                context.write(kp, v);

            }

        }

    }

    public static void main(String[] args) {

        Configuration conf = new Configuration();

        try {

            Job job = new Job(conf);

            job.setJobName("hot");

            job.setJarByClass(RunJob.class);

            job.setMapperClass(HotMapper.class);

            job.setReducerClass(HotReduce.class);

            job.setMapOutputKeyClass(KeyPari.class);

            job.setMapOutputValueClass(Text.class);

            job.setNumReduceTasks(2);

            job.setPartitionerClass(FirstPartition.class);

            job.setSortComparatorClass(SortHot.class);

            job.setGroupingComparatorClass(GroupHot.class);

            //mapreduce输入数据所在的目录或者文件

            FileInputFormat.addInputPath(job, new Path("/usr/input/hot"));

            //mr执行之后的输出数据的目录

            FileOutputFormat.setOutputPath(job, new Path("/usr/output/hot"));

            System.exit(job.waitForCompletion(true) ? 0 : 1);

        } catch (Exception e) {

            e.printStackTrace();

        }

    }

}

　　FirstPartition类：

package com.simope.mr;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Partitioner;

/**

 * 实现分区

 * */

public class FirstPartition extends Partitioner<KeyPari, Text> {

    /**

     * num:reduce数量

     * getPartition()方法的

     * 输入参数：键/值对<key,value>与reducer数量num

     * 输出参数：分配的Reducer编号，这里是result

     * */

    public int getPartition(KeyPari key, Text value, int num) {

        return (key.getYear()) * 127 % num;    //按照年份分区

    }

}

　　SortHot类：

package com.simope.mr;

import org.apache.hadoop.io.WritableComparable;

import org.apache.hadoop.io.WritableComparator;

/**

 * 排序

 * 因为hadoop对数据分组后默认是按照key升序排序的，所以需要自定义排序函数将分组数据降序排序

 * 对于一般的键，只需要key值相同，则对应的value就会分配至同一个 reduce中；

 * 对于复合键，形式为TextPair<key1,key2>，通过控制 key1来进行分区，则具有相同的 key1的值会被划分至同一个分区中，但此时如果 key2不相同，则不同的key2会被划分至不同的分组

 * */

public class SortHot extends WritableComparator {

    public SortHot() {

        super(KeyPari.class, true);

    }

    @SuppressWarnings("rawtypes")

    public int compare(WritableComparable a, WritableComparable b) {

        KeyPari o1 = (KeyPari)a;

        KeyPari o2 = (KeyPari)b;

        int res = Integer.compare(o1.getYear(), o2.getYear());    //升序排序

        if (res != 0) {

            return res;

        }

        return -Integer.compare(o1.getHot(), o2.getHot());    //降序排序

    }

}

　　KeyPari类：

package com.simope.mr;

import java.io.DataInput;

import java.io.DataOutput;

import java.io.IOException;

import org.apache.hadoop.io.WritableComparable;

/**

 * 封装key对象

 * */

public class KeyPari implements WritableComparable<KeyPari>{

    private int year;

    private int hot;

    public int getYear() {

        return year;

    }

    public void setYear(int year) {

        this.year = year;

    }

    public int getHot() {

        return hot;

    }

    public void setHot(int hot) {

        this.hot = hot;

    }

    public void readFields(DataInput in) throws IOException {

        this.year = in.readInt();

        this.hot = in.readInt();

    }

    public void write(DataOutput out) throws IOException {

        out.writeInt(year);

        out.writeInt(hot);

    }

    public int compareTo(KeyPari keyPari) {

        int res = Integer.compare(year, keyPari.getYear());

        if (res != 0) {

            return res;

        }

        return Integer.compare(hot, keyPari.getHot());

    }

    public String toString() {

        return year + "\t" + hot;

    }

    public int hashCode() {

        return new Integer(year + hot).hashCode();

    }

}

　　GroupHot类：

package com.simope.mr;

import org.apache.hadoop.io.WritableComparable;

import org.apache.hadoop.io.WritableComparator;

/**

 * 排序

 * */

public class GroupHot extends WritableComparator {

    public GroupHot() {

        super(KeyPari.class, true);

    }

    @SuppressWarnings("rawtypes")

    public int compare(WritableComparable a, WritableComparable b) {

        KeyPari o1 = (KeyPari)a;

        KeyPari o2 = (KeyPari)b;

        return Integer.compare(o1.getYear(), o2.getYear());    //升序排序

    }

}

初次接触Hadoop,可能代码不是最简,存在可优化的地方还请大家指教。经过第一周无聊的环境部署,终于在这周可以写代码了。。。

如需转载的请注明出处：http://www.cnblogs.com/JimLy-BUG/

Hadoop入门案列，初学者Coder的更多相关文章

初学spring之入门案列
spring其实是一个很大的开源框架,而我学的就是spring framework,这只是spring其中的一小部分.有疑惑的可以去官网去看看,spring官网我就不提供了.一百度肯定有.和sprin ...
Quartz经典入门案列
一.Quartz简介 Quartz是一个开放源码项目,专注于任务调度器,提供了极为广泛的特性如持久化任务,集群和分布式任务等.Spring对Quartz的集成与其对JDK Timer的集成在任务.触发 ...
大数据：Hadoop入门
大数据:Hadoop入门一:什么是大数据什么是大数据: (1.)大数据是指在一定时间内无法用常规软件对其内容进行抓取,管理和处理的数据集合,简而言之就是数据量非常大,大到无法用常规工具进行处理,如 ...
一.hadoop入门须知
目录: 1.hadoop入门须知 2.hadoop环境搭建 3.hadoop mapreduce之WordCount例子 4.idea本地调试hadoop程序 5.hadoop 从mysql中读取数据 ...
大数据技术之_14_Oozie学习_Oozie 的简介+Oozie 的功能模块介绍+Oozie 的部署+Oozie 的使用案列
第1章 Oozie 的简介第2章 Oozie 的功能模块介绍2.1 模块2.2 常用节点第3章 Oozie 的部署3.1 部署 Hadoop(CDH版本的)3.1.1 解压缩 CDH 版本的 hado ...
Hadoop入门概念
Hadoop是分布式系统基础架构,通常指Hadoop生态圈主要解决 1.海量数据的存储 2.海量数据的分析计算优势高可靠性:Hadoop底层维护多个数据副本,即使Hadoop某个计算元素或存储出 ...
Hadoop入门学习笔记---part4
紧接着<Hadoop入门学习笔记---part3>中的继续了解如何用java在程序中操作HDFS. 众所周知,对文件的操作无非是创建,查看,下载,删除.下面我们就开始应用java程序进行操 ...
Hadoop入门学习笔记---part3
2015年元旦,好好学习,天天向上.良好的开端是成功的一半,任何学习都不能中断,只有坚持才会出结果.继续学习Hadoop.冰冻三尺,非一日之寒! 经过Hadoop的伪分布集群环境的搭建,基本对Hado ...
Spring MVC的配置文件(XML)的几个经典案列
1.既然是配置文件版的,那配置文件自然是必不可少,且应该会很复杂,那我们就以一个一个的来慢慢分析这些个经典案列吧! 01.实现Controller /* * 控制器 */ public class M ...

随机推荐

Matlab Colour Theme
[转]http://blog.csdn.net/df865017/article/details/48164429 使用MATLAB进行编码时, 长时间面对白底黑字的屏幕, 眼睛会疼! 因此, 选择一 ...
NEUACM1132: Renew MST Quickly 增量最小生成树
题目链接:http://acm.neu.edu.cn/hustoj/problem.php?id=1132 和UVa11354很类似题意: 原先有一棵树,每次加一条边,看最小生成树大小: 这个和增量 ...
Linux 进程状态标识 Process State Definition
From : http://www.linfo.org/process_state.html 译者:李秋豪进程状态标识是指在进程描述符中状态位的值. 进程,也可被称为任务,是指一个程序运行的实例. ...
【luogu P2071 座位安排】题解
题目链接:https://www.luogu.org/problemnew/show/P2071#sub 邻接表 + 匈牙利把之前的邻接矩阵匈牙利变成邻接表要不然存不下... code: #inc ...
SQL limit
employee 表 id name gender hire_date salary performance manage deparmant 1001 张三男 2/12/1991 00:00:00 ...
swift计算label动态宽度和高度
swift计算label动态宽度和高度 func getLabHeigh(labelStr:String,font:UIFont,width:CGFloat) -> CGFloat { let ...
ABAP术语-Application Server
Application Server 原文:http://www.cnblogs.com/qiangsheng/archive/2007/12/17/1002777.html Server that ...
爬虫——BeautifulSoup4解析器
BeautifulSoup用来解析HTML比较简单,API非常人性化,支持CSS选择器.Python标准库中的HTML解析器,也支持lxml的XML解析器. 其相较与正则而言,使用更加简单. 示例: ...
checked="checked"无效，radio未选中问题排查
在使用attr给元素设置属性的时候,但是显示在页面就是未选中,而且是只使用一两次是ok的,但是连着多使用就不起作用了,百度了下,发现遇到这个问题的人还蛮多的, 有人说,发现在jQuery1.6版本之后 ...
javascript--自定义弹出登陆窗口（弹出窗）
web开发中浏览器对象封装了诸如prompt.alert.confirm等弹出框,但是有的弹出框并不能满足开发需要,需要我们自己定义弹出框,诸如用户登陆框.消息提示框等.本文利用弹出用户登陆框示例,对 ...

Hadoop入门案列，初学者Coder

Hadoop入门案列，初学者Coder的更多相关文章

随机推荐

热门专题